whisper-large-v3-q4

whisper-large-v3-q4

whisper-large-v3-q4 is an MLX-ready Whisper speech-to-text checkpoint derived from openai/whisper-large-v3 for local transcription on Apple Silicon.

Intended use

• Local speech-to-text transcription on Apple Silicon
• Batch or interactive audio transcription experiments
• Multilingual ASR workflows when supported by the upstream Whisper checkpoint

Out of scope

• Safety-critical decisions without domain expert review
• Claims of benchmark superiority not backed by published evaluation data
• Non-MLX runtime guarantees; this card documents the shipped HF checkpoint, not every possible serving stack
• Speaker diarization, clinical interpretation, or audio enhancement

Training and conversion metadata

This card only reports metadata present in the Hugging Face repository, existing card frontmatter, or public config files. Missing benchmark, dataset, or training-run details are left explicit rather than reconstructed.

Tested inference path

Inference for this checkpoint has been tested with LibraxisAI/mlx-batch-server.
This is the recommended tested path for operator-controlled local inference on Apple Silicon.

This does not claim compatibility with every possible serving stack. It documents the path that has been exercised for this published checkpoint.

Usage

Python

import mlx_whisper

result = mlx_whisper.transcribe(
    "audio.wav",
    path_or_hf_repo="LibraxisAI/whisper-large-v3-q4",
)
print(result["text"])

Notes

• Use local audio files supported by mlx_whisper.
• For long recordings, split audio into manageable chunks before transcription.

Example output

No public sample output is currently declared for this checkpoint.

Quantization notes

Limitations

• No public benchmarks for this checkpoint are declared in the model metadata.
• No public benchmark claims are made by this card unless listed in the frontmatter.
• Validate outputs on your own domain data before relying on this checkpoint.
• Memory use and speed depend heavily on Apple Silicon generation, unified-memory size, audio duration, and language complexity.

License

mit. Check the upstream/base model license as well when a base model is declared.

Citation

@misc{libraxisai-whisper-large-v3-q4,
  title = {whisper-large-v3-q4},
  author = {LibraxisAI},
  year = {2026},
  howpublished = {\url{https://huggingface.co/LibraxisAI/whisper-large-v3-q4}},
  note = {MLX checkpoint published by LibraxisAI}
}

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